Program notes and translations for a graduate voice recital

"Ghi ii sole dal Gange" "O cessate dipiagarmi" "Spesso vibra per suo gioco" Alessandro Scarlatti (1660-1725) "Must the winter come so soon"? from Vanessa Samuel Barber (1910-1981) Two Songs from Mirabai Songs "It's True I Went to the Market" "Don t Go, Don t Go" John Harbison (b.1938) El amor brujo "Cancidn del amor dolido" "Cancidn delfuegofatuo" "Danza del juego del amor" "Las campanas del amanecer " Manuel de Falla (18 76-1946) INTERMISSION "Standchen" from Leise flehen meine Lieder "Du bist die Ruh" D776 "Gretchen am Spinnrade" D257 Franz Schubert (1797-1828) "Una voce pocofa" from Il barbiere di Siviglia Gioachino Rossini (1792-1868) La Regata Veneziana - Three songs in Venetian dialect "Anzoleta avanti la regata" "Anzoleta co passa la regata" "Anzoleta dopo la regata"

Extended program notes for a recital of selected vocal literature

The purpose of these extended program notes is to provide information that will assist the listening audience in comprehending the historical and biographical significance of the each vocal selection. A brief analysis of each selection, a historical interpretation of the work, a translation of the texts and a CD of the recital are included. The contents of the recital comprise of several selections from the soprano repertoire: The Georg Phillip Telemann cantata Lauter Wonne, lauter Freude; the Wolfgang Amadeus Mozart Laudate Dominum from the Vesperae solemnes de confessore; the Joaquin Nin y Castellano Diez Villancicos de Noel; Gabriel Faurd's art songs Dans le ruines d'une abbay, Les Berceaux, and Au bord e l'eau; Sergei Rachmaninov's three songs Oni otvechali, Zdes Khorosho, and Vocalise; and Libby Larsen's Cowboy Songs.

The bass as a melodic voice in jazz : extended program notes for thesis recital

This thesis discusses the use of the bass as a melodic instrument in jazz. It focuses on seven compositions performed for a Master's recital on March 22, 2010. For each selection, I provide a brief biography of the composer, information about the song and insight on performance practice. I examine the advanced techniques pioneered by innovative bassists and explore ways in which they can be used to further exploit the melodic potential of the bass in a jazz context. A compact disc recording of the recital is included.

Piano recital consisting of works by J. S. Bach, L. Van Beethoven and F. Chopin with extended program notes

Partita No 4 in D Major, BWV 828 - Johann Sebastian Bach (1685-1750) Sonata No 23 in F minor, Op 57 - Ludwig van Beethoven (1770-1827) Scherzo No 1 in B minor, Op 20 - Frederic Chopin (1810-1849)

Music teacher biography and its impact on teaching practice

This paper describes research carried out as part of a wider doctoral study on ‘the biography of music teachers, their understanding of musicality and the implications for secondary music education’. Music teachers will come from a range of diverse backgrounds, though research data would suggest that most seem to have been educated as ‘classical’ music performers which will have an affect on what they perceive to be central competencies in the development of young musicians. In turn, this will determine, to some extent, what is taught and learned in the secondary music classroom. This study explores the impact of the biography of secondary music teachers as they seek to develop the musicianship of their pupils and present the activities in which the young people will be expected to participate. A mixed methods approach has been taken, including surveys, observation and interviews. Surveys amongst a sample of experienced and trainee teachers have produced a range of quantitative data on respondents’ experience of and values related to music education; whilst qualitative data in the form of lesson observation notes and transcription of semi-structured interviews have been the result of working with a small sub-set of participants. The outcomes of study have suggested a clear link between biography and classroom practice but that there are also other potential tensions which arise, such as in the subject knowledge development of practitioners as they move from musician to teacher. Implications for a variety of stakeholders in secondary music education include a consideration of the development of subject knowledge together with potential review of national and local education policy, the nature of undergraduate music study and the ‘shape’ of initial teacher training in England.

Exploring Armenian keyboard music : roots to modern times

The extended program notes include historical facts of the composers and characteristics of the pieces being performed. The thesis also includes information about Armenian composers starting from 18th to the 20th century, composition's historical background, brief biographies of the composers as well as analysis of form and structure. The graduate piano recital comprised the following compositions: Sayat Nova - R. Andriasian Yes Mi Kharib Blbuli Pes; Komitas - R. Andriasian Garun a, Shoker Jan, Dzirani Dzar, Gakavik; A. Khachaturyan Poem; A. Babadjanyan Elegy in Commemoration of A. Khachaturyan; E. Bagdasarian Humoresque, Prelude in D Minor, Prelude in B Minor; A. Babadjanyan Improvisation and Traditional from six Pictures; A. Babadjanyan Prelude and Vagarshapat Dance; A. Arutyunian Dance of Sasoon; A. Arutyunian - A. Babadjanyan Armenian Rhapsody for Two Pianos.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.